a fusion approach to retrieve soil moisture with sar and optical

196 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 5, NO. 1, FEBRUARY 2012

A Fusion Approach to Retrieve Soil MoistureWith SAR and Optical Data

Rishi Prakash, Dharmendra Singh, Senior Member, IEEE, and Nagendra P. Pathak

Abstract—The retrieval of soil moisture in vegetated areas withactive microwave remote sensing is a challenging process becausescattering form the vegetated area incorporates the volume scat-tering from the vegetation cover and surface scattering from theunderlying soil. In addition to this, vegetation provides two way at-tenuation for the signal scattering from the underlying soil. There-fore, retrieval of soil moisture requires such an approach that mayadequately represent the scattering behavior from the vegetationcovered area by defining the scattering term from the vegetationand vegetation covered soil clearly. Characterization of scatteringdue to vegetation is another cumbersome and complex process be-cause it needs several vegetation parameters as input and impor-tant problem is that these vegetation parameters exhibit temporalbehavior. Therefore, it is the need of present scenario to look forsuch an alternate approach that may not require the scatteringcharacterization of the concerned vegetation moreover employsthe ancillary information. Normalized difference vegetation index(NDVI) which can be obtained with optical data and is an indicatorof vegetation,may be efficiently employedwith SAR for retrieval ofsoil moisture in the vegetated area. With this aspect, present paperaims to fuse the information from PALSAR (Phased Array typeL-band Synthetic Aperture Radar) and MODIS (Moderate Reso-lution Imaging Spectroradiometer) data to develop an algorithmfor soil moisture retrieval in vegetation covered area. A knowledgebased approach involving various polarizations (linear, circular,linear 45 , co- and cross-polarized ratios for both linear and cir-cular polarization) is used for land cover classification by whichurban and water area can be masked. A normalized scatteringbased empirical model is developed where normalized coefficient isa function of vegetation characteristic (i.e., NDVI). The developedrelationship provides the scattering coefficient of bare soil in HH-and VV-polarization and these values were subsequently used inDubois Model, which has been solved with copolarization ratio ap-proach to provide volumetric soil moisture content irrespective ofroughness value. Two sets of images were used to test and validatethe developed algorithm. The obtained results are in good agree-ment with the ground truth values and have potential to apply forsoil moisture retrieval in large scale.

Index Terms—Soil moisture, synthetic aperture radar, remotesensing.

Manuscript received April 02, 2011; revised June 26, 2011 and August 30,2011; accepted September 02, 2011. Date of publication November 01, 2011;date of current version February 29, 2012. This work was supported by the De-partment of Earth Sciences, Delhi, India and the Department of Science andTechnology, India.The authors are with the Department of Electronics and Computer Engi-

neering, Indian Institute of Technology Roorkee, Roorkee 247667, India (cor-responding author, e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/JSTARS.2011.2169236

I. INTRODUCTION

E STIMATION of spatial distribution of soil moisture is im-portant in many applications such as hydrology, meteo-

rology, agronomy, climatology and many other earth sciencesapplications [1]. Active microwave remote sensing provides usavenue to estimate the spatial distribution of soil moisture overa large area [2]–[9]. Alternatively, these soil parameters can bemeasured by various methods. The most direct methods are insitu measurements. These methods are quite accurate as wellas provide good estimates of soil parameters, but they are pointmeasurements. Therefore, it is very difficult to generalize the es-timate of soil parameters for large areas of study from such pointestimates because of the spatial variability in soil parameter atsmall scales. Also, the spatial coverage of in situ measurementsis usually limited.Most of the techniques for soil moisture retrieval through ac-

tive microwave remote sensing have been developed for baresoil [5], [10]–[13]. These techniques cannot be applied directlyin the vegetated areas as the vegetation provides the multiplescattering effects. The observed backscatter is highly nonlineardue to this multiple scattering effect [14]. The problem arisesin the separation of the scattering contribution of the vegeta-tion and scattering contribution of the vegetation covered soilmoisture from the observed backscattering coefficient. Someadvancement has been made to characterize the vegetation butstill complexities exist. Most of the technique to retrieve thesoil moisture in presence of vegetation utilizes semi-empiricalwater cloud model [15]–[17]. Water cloud model represents thecanopy as a cloud of water droplet and higher order scatteringcontribution are neglected. Bindlish and Barros [16] incorpo-rated water cloud model to retrieve the soil moisture in vege-tated area. They have introduced the concept of the vegetationcorrelation length to model the vegetation spacing within thewater cloud model. To implement the proposed model severalvegetation parameter have to be estimated or one should have apriori information of these vegetation parameters. Xu et al. [17]has utilized the water cloud model to remove the vegetation ef-fect from the observed backscattering coefficient, while havingthe knowledge of vegetation parameters, i.e., vegetation height,vegetation water content etc. Several other researchers have alsoattempted to retrieve the soil moisture while utilizing the watercloud model to characterize scattering from vegetation but themeasurement of the vegetation parameters is of main concernin the applicability of these approaches. The vegetation param-eters show temporal behavior and to characterize these param-eters field visits have to make each time. Also, these parame-ters differ from vegetation to vegetation. The other techniques

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PRAKASH et al.: A FUSION APPROACH TO RETRIEVE SOIL MOISTURE WITH SAR AND OPTICAL DATA 197

used frequently for soil moisture retrieval in vegetated areas arethe change detection techniques. These techniques consider thescattering from vegetation area to be time invariant whereas thescattering contribution from the vegetation cover is very muchdependent on the vegetation parameters and these parametersare time variant therefore the scattering contribution from thevegetation cover will be a time variant process and it will re-strict the applicability of the change detection techniques [18].The problem of vegetation parameterization in soil moisture

retrieval has been primarily dealt with the microwave data bycharacterizing the scattering behavior. Whereas, some otherform of the remote sensing can also be exploited to parame-terize the vegetation and subsequently these information maybe fused with the microwave data to retrieve the soil moisture.Optical data have been utilized to parameterize the vegetation.The red, near-infrared (NIR) and shortwave-infrared (SWIR),are commonly employed to the retrieve the vegetation watercontent, canopy height, leaf area index etc [19]–[21]. Opticaldata with fusion of SAR data is used to yield the vegetationinformation [22], [23]. These studies led us to conclude thatthe information available with optical data may be efficientlyemployed with SAR data to provide the soil moisture informa-tion in vegetated areas. Some studies have been performed touse SAR and optical data in soil moisture retrieval [14], [24].The fusion of SAR and optical data has also been considered inurban mapping [25], [26]. Wang et al. [24] made use of ERS-2and TM imagery for retrieval of soil moisture. They haveused multi-temporal ERS data, i.e., one image of dry seasonand the other of wet season. The dry season image was usedto normalize the wet season image and the several empiricalrelationships with NDVI were developed based on isomoisturelines. The developed empirical relationship does not providethe absolute value of soil moisture rather it gives a range withinwhich the moisture values may lie. Further, the assumptionhas been made that the surface roughness in the wet seasonand the dry season do not change remarkably, which limits itsapplicability. Notarnicola et al. [14] calculated the probabilitydensity function (pdf) for different vegetation and then inves-tigated its correlation with normalized difference water index(NDWI). This information has been used in inversion model toretrieve the soil moisture. The developed model employs pdfand values of pdf differ from vegetation to vegetation. So, itmay be difficult to get pdf of various vegetations in each stageof vegetation growth.Characterization of soil surface roughness is another area of

concern in soil moisture retrieval studies. In case of scatteringfrom bare soil, scattering coefficient is a function of soil mois-ture and surface roughness besides the sensor parameters. Sev-eral researchers have proposed the use ofmulti-polarization datato minimize the roughness effect in order to retrieve the soilmoisture without characterizing surface roughness [27]–[30].Franceschetti et al. [27] has shown the potential of theoret-ical models like small perturbation model (SPM) and Kirch-hoff scalar approximation (KA) to retrieve the dielectric con-stant with minimizing the roughness effect by copolarizationratio approach. Whereas, Ceraldi et al. [29] and Prakash et al.[30] have shown the strength of copolarization ratio for min-

imization of roughness effect in bistatic domain with theoret-ical models, e.g., small perturbation model (SPM), Kirchhoffstationary phase approximation (SPA) and Kirchhoff scalar ap-proximation (SA). Magagi and Kerr [28] investigated the semi-empirical model developed by Oh et al. [13] to minimize thesoil roughness effect in soil moisture retrieval by copolarizationratio approach. Therefore, the use of SAR data with both like po-larization, i.e., HH- and VV-polarization, (e.g., PALSAR) willprovide an upper hand in soil moisture retrieval in comparisonto single polarization SAR data. So, the objective of this paperis to analyze the feasibility of relating the information availablefrom SAR data and optical data to envisage such an approachthat mostly rely on the information content of satellite data andrequire minimum a priori information to retrieve the vegeta-tion covered soil moisture. The concept of such an approacharises as the vegetation can be modeled through the SAR dataas well as the optical data. In case of SAR data the backscat-tering is affected by the vegetation cover and contains the in-formation regarding vegetation whereas, the normalized differ-ence vegetation index (NDVI) provides a good estimate of thevegetation cover. The utilization of information content fromoptical data reduces the requirement of a priori informationwhich is required in vegetation parameters characterization. So,in this paper an empirical relationship has been developed be-tween normalized scattering coefficient and NDVI to incorpo-rate the vegetation effects in retrieval of vegetation covered soilmoisture.The paper has been organized as follows. Section II provides

the brief discussion about the data used along with the studyarea. The preprocessing of the data acquired is presented inSection III. Section IV deals with the model development phaseand discuss in details the various steps employed in algorithmdevelopment. This section also describes the vegetation param-eterization in PALSAR and MODIS data. The implementationof developed algorithm on test region image as well as on val-idation region image has been discussed in Section V which isfollowed by the conclusion in Section VI.

II. STUDY AREA AND DATA USED

Two sets of satellite images comprising Roorkee city,Manglaur town (Uttarakhand, India) and its surrounding areaswere chosen for the study. The satellite images used for the de-velopment of algorithm and its validation are ALOS-PALSAR(Advanced Land Observation Satellite—Phased Array typeL-band Synthetic Aperture Radar), a SAR data and MODIS(Moderate-resolution Imaging Spectroradiometer), an opticaldata. Details of these data along with their latitude and lon-gitude are provided in Table I. The PALSAR image is fullpolarimetric L-band (1.27 GHz) image with size of pixel 25m by 25 m and the MODIS data used is of band 1 (620–670nm) and band 2 (841–847 nm) with size of pixel 250 m by250 m. PAL-1 and MOD-1 (Table I) data sets were used forthe algorithm development as well as testing the developedalgorithm whereas, PAL-2 and MOD-2 (Table I) data sets wereused for validating the developed algorithm. The test area aswell as the validating area is fairly flat with approximatelysimilar land cover classes and mostly consists of the urban,


TABLE IDETAILS OF PALSAR AND MODIS DATA USED FOR THE STUDY

water and agriculture lands. The major water bodies are SolaniRiver and Upper Ganga Canal whereas sugarcane and wheatwere prominent vegetation cover in agriculture land along withthe barren land at the time of image acquisition (first week ofApril 2009). The key urban areas in the study site are Roorkeecity and Manglaur town besides the small villages scatteredthroughout the study site.The ground survey was carried out for 60 areas, in which

30 belongs to the test area (PAL-1 image) and remaining 30areas belongs PAL-2 image which is used for validation. Thesurvey was carried out to measure the soil moisture and surfaceroughness. Eight to ten samples of each area were taken for themeasurement.

III. PREPROCESSING OF PALSAR AND MODIS DATA

The level 1.1 VEXCEL format data of ALOS-PALSAR areused for this paper. The data are provided by the ERSDAC(Earth Remote Sensing Data Analysis Center) which is singlelook complex slant range full polarimetric focused data. Thepreprocessing steps i.e., polarimetric calibration, polarizationsynthesis, speckle filtering with Wishart Gamma map filter,multilooking and geocoding were performed with SARSCAPE4.1, dedicated SAR data processing software developed bySARMAP and works with the integration with ENVI (Environ-ment for Visualizing Images).MODIS data was acquired from sensor Terra for 8 days com-

posite reflectance product (MOD09) at 250 m spatial resolution.This contains the red (645 nm) and near-infrared (858 nm) sur-face reflectance product. The acquired MOD09 data representthe reflectance values that would be measured at the land sur-face if there were no atmosphere. This data is already correctedfor the effect of gaseous absorption, molecules and aerosol scat-tering. The MOD09 product is distributed in a sinusoidal gridprojection (SIN). Therefore, the MODIS band 1 and band 2 datawas geometrically rectified to UTM coordinates [31].

IV. MODEL DEVELOPMENT

A flow chart is given in Fig. 1 for the retrieval of soil moisturein the vegetated areas. Following sections will discuss the detailprocedure of model development.

A. Classification of PALSAR Image With Decision TreeClassifier

The aim of this paper is to develop an algorithm for retrievalof soil moisture in vegetated areas. In study areas, there is noclear cut demarcation between the various land cover classes,i.e., it is very difficult to find out the agriculture or urban areafor a large stretch (tens of kilometers) therefore, there is alwaysthe possibility to find mixed land cover classes. In general, onemay obtain the urban areas (e.g., villages) along with the waterbodies (e.g., canals, ponds) within the stretch of agriculture landand such type of other mixed classes. Due to this, satellite imageof any area of interest may contain all these mixed land coverclasses. This arise the need as a prerequisite to classify the imagein various land cover classes so that the region of interest inthe image can be clearly marked. In present case, a knowl-edge based approach has been applied to classify the PALSARimage and find out the vegetated and bare land remarkably. ThePALSAR image classification has been carried out with the de-cision tree classifier [32]. It is an efficient tool for land coverclassification and based on data mining technique [32]–[34].The decision tree classifier uses knowledge based approach thatis developed through knowledge of data obtained by empir-ical evidence and their experimental validation. Fig. 2 illus-trates the flow chart of the classification scheme. Backscatteringcoefficient from various polarization characteristic (linear, cir-cular, linear 45 , co- and cross-polarized ratios for both linearand circular polarization) were utilized to classify the image inurban, water, vegetated land (tall vegetation and short vegeta-tion) and bare soil. The decision limits in the knowledge basedapproach are set on the basis of empirical evidence and the ex-perimental validation. First of all, the water bodies were distin-guished from other classes by the conditionand . The case, when and

[11], [33], the classified area can be


Fig. 1. Flow chart for the retrieval of soil moisture.

characterized as tall vegetation or urban. Therefore, urban andtall vegetation were separated on the basis of cross polariza-tion ratio of circular polarization which is negative for urbanand positive for tall vegetation [34]. The bare soil which exhibitthe surface scattering can be distinguished with the condition

, and[33], [35]. The short vegetation can be classified based on thecriterion greater than or equal to 11 dB andis less than or equal to 18 db [11]. Pixels that do not satisfyany of these conditions are termed as unclassified. The devel-oped classification algorithm was tested on pixel-by-pixel basis[32].

B. Vegetation Modeling in SAR Data

First step of the algorithm development is to model vegeta-tion with scattering coefficient of the obtained image. Attemaand Ulaby has proposed a semi-imperial approach based onwater cloud to determine the backscattering coefficient of veg-etation covered area [36]. This approach requires a priori infor-

Fig. 2. Flow chart for the decision tree classifier.

mation of vegetation parameters. These vegetation parametersmay include vegetation height, vegetation water content, leafarea index, biomass etc. Therefore, as an alternative to the veg-etation parameterization, we have utilized a more simplified ap-proach in the form of normalizing the backscattering coefficientof the SAR image where the need of vegetation parameteriza-tion is not required [37]. The requirement of vegetation charac-terization is fulfilled with the utilization of the optical data. Thisapproach mostly relies on the information obtained from SARand optical image.In general, the scattering coefficient depends on sensor pa-

rameters and target parameters. Now in particular, if the sensorparameters are fixed, the scattering coefficient will depend onthe target parameters, e.g., in case of vegetated area, scatteringcoefficient will depend on vegetation characteristic as well asunderlying soil parameters and will be represented as.

(1)

where represents the scattering coefficient of SARimage of vegetated area. The scattering coefficient of the baresoil depends on the soil parameters, i.e., soil moisture and sur-face roughness and represented as

(2)

where represents the scattering coefficient of the baresoil. Equation (1) and (2) suggest that if we normalize thescattering coefficient of the vegetated areas to the scatteringcoefficient of the bare soil, the normalized value will be mainlythe function of the vegetation characteristic [18], [37]. In thecase of PALSAR image, the observation frequency is fixedthroughout the image acquisition whereas incidence angledepends on the local topography. The study area consideredfor the present study is fairly flat therefore the incidence angleis considered constant throughout the study area. The differentimages available for the same area of interest are with thedifferent polarization (HH-, HV-, VH- and VV-polarization).So that, in the case of PALSAR image, with the availability


of different polarization images, the normalized scatteringcoefficient can be written as

(3)

where the PP represents the polarization. In the present case,HH- and VV-polarization has been considered for the develop-ment of the soil moisture retrieval in vegetation covered areas.Therefore, PP will represent either HH- or VV-polarization.1) Computation of Bare Soil Backscattering Coefficient: The

methodology developed in Section IV-B (3) to characterize thevegetation with the normalized scattering coefficient will re-quire a priori knowledge of scattering coefficient of bare soil atthe time of model development. Therefore, 30 test areas werechosen for in situ measurement of soil moisture and surfaceroughness under the vegetation cover at the time of image ac-quisition. Backscattering coefficient of bare soil was computedwith the help of Dubois et al. [11] model ((4) and (5)) [11].Dubois et al. [11] model has been used because theoretical

models such as SPM, SA and SPA [38] are very much limitedto the roughness and moisture range and IEM is quite complexmodel. Therefore, there is a need of such model that is less com-plex, has wide validity range and tested globally. Oh et al. [13]and Dubois et al. [11] models are broadly used semi-empiricalmodels. Several researchers have applied and validated thesemodels [39]–[47]. Oh et al. [13] model relates the dielectric con-stant of soil and rms surface height to the copolarization ratio

and crosspolarization ratiowhereas, Dubois et al. [11] developed a model that relates thescattering coefficient in HH-polarization and VV-polar-ization to the dielectric constant of soil and rms surfaceheight. We have utilized the Dubois et al. [11] model as it pro-vides the direct relationship between the scattering coefficient inHH- andVV-polarization and soil parameters (i.e., soil moistureand surface roughness). Dubois et al. [11] model can be solvedto provide the dielectric constant of soil as function of scatteringcoefficient in HH- and VV-polarization, therefore does not re-quire the characterization of soil surface roughness (details aregiven in Section IV-E). The knowledge of surface roughnessis required at the time of algorithm development and once thealgorithm is developed, the retrieval of soil moisture in vege-tated areas can be obtained from HH- and VV-polarized im-ages without requirement of surface roughness values. There-fore, we have selected Dubois model for our study. The scat-tering coefficient in HH-polarization and VV-polar-ization ) is given as

(4)

(5)

where , , , and represent rms surface height, dielectricconstant, incidence angle, wavelength and wave number respec-tively. Incidence angle and wavelength are 24 and 23.6 cm, re-spectively for PALSAR image of the study area. The dielectricconstant of soil can be measured with the empirical relation-ship given by [48]

(6)

where is the volumetric soil moisture constant.This empirical equation is independent of soil type, bulk density,texture and temperature of the soil [48], [49].Field survey was carried out to measure the soil moisture and

surface roughness. Eight to ten samples were collected fromeach test area and volumetric soil moisture was computedusing [50]

(7)

where and are weight of moist and dry soil samplerespectively and is the soil bulk density. To measure theweight of dry soil sample , the moist soil samples weredried for 24 hours at 110 . The observed soil moisture rangesfrom 0.042 to 0.244 .The soil surface roughness was retrieved with the help of pin

profilometer [51]. The roughness of the field during the obser-vation was found to be approximately constant and the averagerms surface height was observed 0.53 cm with standard devia-tion 0.06 cm.

C. NDVI as the Vegetation Parameter

The Normalized Difference Vegetation Index (NDVI) is al-most linearly related to the vegetation abundance and thereforeit can represent the vegetation effect in the soil moisture re-trieval studies [24]. NDVI is defined as the ratio of the differenceand sum of the spectral response at the infrared wavelength andred wavelength as

(8)

where and are the reflectance at NIR band and REDband respectively.The possible ranges of the NDVI values are 1 to 1. In

case of vegetation the NDVI values typically ranges from 0.1 to0.6, where the lower values indicates the lower density and thehigher values represent the higher density as more greennessof the vegetation. The negative values of the NDVI representthe water bodies whereas the values approximate to zero indi-cate the presence of the soil and rock. The phenomenon of sat-uration in NDVI values generally occurs for dense forest area.The NDVI values saturates approximately at 0.6 [52]. The studyarea which we have considered contains the agriculture landwhere the NDVI values vary from 0.25 to 0.44. In present paper,NDVI values are representing the vegetation abundance whichis used as a vegetation parameter in algorithm development inassociation with the normalized scattering coefficient that hasbeen retrieved through the PALSAR image. NDVI values havebeen calculated with MODIS band 1 (red) and band 2 (near-in-frared) images. The NDVI values of vegetated areas for MOD-1data vary from 0.25 to 0.43. Similarly, for MOD-2 data, theNDVI values of vegetated area vary from 0.25 to 0.44. Thelower values of the NDVI signify the lower density of vege-tation whereas the higher values signify the higher density ofvegetation.

D. Development of Relationship Between and NDVI

This section investigates the relationship between the nor-malized scattering coefficient ((3)) and NDVI ((8)). Test im-ages (PAL-1 and MOD-1) were used to monitor the change in


normalized scattering coefficient with NDVI and 30 test areaswere taken into account to develop the empirical relationships.Changes in with the NDVI have been checked as NDVIis the descriptor of the vegetation and normalized scattering co-efficient explains the vegetation effect in SAR (as discussed inSections IV-C and IV-B, respectively). The NDVI values havebeen obtained through the MODIS data and have beenretrieved through the PALSAR product. The spatial resolutionof the PALSAR data in polaremetric mode is 25 m whereasthe spatial resolution of the MODIS data is 250 m. Therefore,PALSAR pixel values have been averaged to match the resolu-tion of MODIS data to make PALSAR data pixel values rep-resentative to MODIS data pixel values. In the development ofthe empirical relationship between NDVI and , the con-sidered value of the is in decibel (dB). The developedempirical relationship is given as

(9)

where , and are the empirical coefficient and thesevalues will differ for the different polarization, i.e., , andwill have different constant values in HH-polarization and

some other constant values in VV-polarization (The detailed de-scription of empirical coefficient along with its values are givenin Section V-A). The constant is in dB.

E. Retrieval of Soil Moisture

This section will discuss the retrieval of soil moisture inthe vegetated area with the use of PALSAR and NDVI data.The relationship between and NDVI developed inSection IV-D ((9)) will provide the value of (dB)with the known values of NDVI from the correspondingMODIS image. The bare soil scattering coefficient in HH-and VV-polarization can be computed with the relationship

((3)). If we consider indB, the scattering coefficient of soil is given by

(10)

Therefore, the scattering coefficient of soil in the form of empir-ical relationship developed in Section IV-D ((9)) can be givenby (11).

(11)

Equation (11) will provide the scattering coefficient ofbare soil in HH- and VV-polarization with the normalizedscattering coefficient obtained through the PALSAR data andNDVI values obtained through the MODIS data. The retrievedscattering coefficient values of the bare soil will contain theinformation of the soil moisture as well as surface roughness.Therefore, to minimize the roughness effect in the retrieval

of soil moisture, copolarization approach has been utilized[27], [30] and Dubois equations in HH- and VV-polarizationhave been solved to provide an equation that is the function ofdielectric constant only, and independent to surface roughness.This solution of equation will provide the flexibility to apply theretrieval algorithm over a wide range of roughness conditionswithout requirement of characterizing the surface roughness.The solution is given by (12), shown at the bottom of the page,where

and

Dubois equations have been solved to provide the dielectricconstant of the soil (12) and volumetric soil moisture can becomputed using [38]

(13)

V. STEPS OF THE IMPLEMENTATION

A. Implementation on the Test Image

Implementation of proposed algorithm is discussed in thissection. PAL-1 and MOD-1 data have been used for develop-ment of algorithm. Firstly, PALSAR image of the test region(PAL-1) is classified into various land cover features, accordingto the procedure discussed in Section IV-A. Fig. 3(a) shows thecomposite PALSAR image (HH, HV, and VV)whereas Fig. 3(b)shows corresponding classified PALSAR image (classificationhas been carried out with decision tree method as proposed inflow chart Fig. 2). Image has been classified into urban, water,short vegetation, long vegetation and bare soil. The overall clas-sification accuracy was 92.52%. To estimate the classificationaccuracy, extensive ground truth survey was performed over thewhole region. Around 720 Ground truth points (GCP) were col-lected for training and testing the accuracy of classificationmap.After obtaining the classified image, the urban and water regionof the image are masked. The masking is performed with the in-tent to demarcate the vegetation and bare soil region, as the algo-rithm has been developed for the vegetated and bare soil areasfor soil moisture retrieval. Fig. 4 shows the classified maskedimage. The size of considered PALSAR test (PAL-1) image is850 850 pixels, i.e., the total number of pixels were 722,500.After masking the image, 515,313 pixels were corresponding tothe vegetated area and 11,084 pixels were corresponding to thebare soil. Fig. 5 shows the corresponding NDVI image retrievedby band-1 and band-2 of MOD-1 image. The NDVI values ofthe test region ranges from 0.25 to 0.43. Due to the difference

(12)


Fig. 3. PALSAR test image (PAL–1, pixel spacing = 25 m). (a) Color com-posite image (HH = red, HV = green, VV = blue). (b) Classified image (red =urban, blue = water, green = short vegetation, sea green = long vegetation, andsienna = bare soil).

Fig. 4. Classifiedmasked image of the PAL-1 pixel spacing m showingsome of the test areas (in circle) used for in situ measurment of the soil moistureand surface roughness for development of algorithm.

in the spatial resolution of the PALSAR and MODIS data, a 10by 10 averaging of PALSAR image is carried out to match thecorresponding pixel of MODIS image.The variation of the normalized scattering coefficient in the

HH-polarization and VV-polarization withNDVI is shown in Figs. 6(a) and 6(b) respectively. The increasein normalized scattering coefficient with increase in NDVI can

Fig. 5. NDVI image of test area pixel spacing m .

be observed in HH-polarization as well as VV-polarization. Theincrease in normalized scattering coefficient is more for lowervalue of NDVI (i.e., 0.26 to 0.34) whereas for higher value ofNDVI, there is lesser change in normalized scattering coeffi-cient. This behavior may explained as the lower NDVI valuesrepresent lower density of vegetation and higher value representthe high density of vegetation which causes lesser change in nor-malized scattering coefficient. The trend of change in normal-ized scattering coefficient with NDVI is similar in HH-polariza-tion and VV-polarization. The change in the normalized scat-tering coefficient with the NDVI led to the development of theempirical relation as given in (9) (Section IV-D). The develop-ment of the empirical relationship requires in situ measurementof the soil moisture and surface roughness to calculate the scat-tering coefficient of the bare soil in HH- and VV-polarizationand subsequently these values are used to normalize the scat-tering coefficient of vegetated areas obtained from the PALSARimages in HH- and VV-polarization, respectively. Therefore,30 test areas were selected for the measurement of soil mois-ture and surface roughness which consists of different types ofvegetation (sugarcane, wheat and vegetables). Some of the testareas are marked in Fig. 4. The developed empirical relation-ship is quadric in HH-polarization as well as in VV-polarization,except the difference in the empirical coefficients. The empir-ical coefficients , and in case of the HH-polarizationare 218.1, 172.2 and 38.41 respectively with coefficient ofdetermination 0.83 and root mean square error (RMSE)0.5313. Further, in case of the VV-polarization, , andare 241.7, 201.3 and 41.8 respectively withand . The developed empirical relationshipwill facilitate to retrieve (dB) and (dB) with theNDVI values of the test image. These normalized scatteringcoefficients in conjunction with the scattering coefficients ofPALSAR image ( (dB) and (dB)) will pro-vide the scattering coefficient of the soil ( (dB) and

(dB)). Subsequently, the dielectric constant of the soilis retrieved with (12) and the corresponding volumetric soilmoisture constant is obtained from (13). The retrieved soil mois-ture map is shown in Fig. 7. Fig. 8 shows the graph between thevolumetric soil moisture values retrieved through the developed


Fig. 6. Response of normalized scattering coefficient (a) in HH-polarizationand (b) in VV-polarization.

Fig. 7. Soil moisture (volumetric) map of the test image (PAL-1) where pixelwith black color show the masked area. The pixel spacing for the generated soilmoisture map is 250 m.

algorithm and the observed volumetric soil moisture values. Theroot mean square error (RMSE) for the retrieval of the soil mois-ture is 0.036.

B. Validation of Algorithm

To validate the developed algorithm second set of the images(PAL-2 and MOD-2) were used. The area of study used to vali-date the developed algorithm is fairly flat and the land cover was

Fig. 8. Observed and retrieved value of volumetric soil moistuer for the testarea.

approximately similar to the land cover of test images (PAL-1and MOD-1). The field survey of 30 test area was carried out tomeasure the soil moisture and surface roughness. The observedsoil moisture ranges from 0.049 to 0.238 .The surface roughness of all the test area is approximately con-stant and the average value was 0.56 cm. The classification ofthe PALSAR image of the validating region (PAL-2) was carriedout with the procedure laid down in Section IV-A and image wasclassified into urban, water, short vegetation, long vegetationand bare soil. Fig. 9(a) shows the composite PALSAR image(HH, HV and VV) whereas corresponding classified PALSARimage is shown in Fig. 9(b) and Fig. 9(c) shows the maskedimage (urban and water region masked). The validating imagewas also of size 850 850 containing 722500 pixels. Aftermasking the image the number of pixel for vegetation areasand bare soil are 595653 and 12091 respectively. Fig. 10 showthe corresponding NDVI image retrieved through the MODISdata, which varies from 0.25 to 0.44. The PALSAR pixels cor-responding to the MODIS pixels were averaged as the proce-dure was performed in the case of test PALSAR image, i.e.,PAL-1. The empirical relationship developed for the test regionwas used in case of validating region also (i.e., the retrievedempirical coefficient , , and were same as for the testimage) and similar procedure was carried out to retrieve the soilmoisture values. The retrieved soil moisture image is shown inFig. 11 whereas Fig. 12 shows the relationship between the vol-umetric soil values retrieved using developed algorithm and ob-served volumetric soil moisture values. The root mean squareerror (RMSE) for the retrieval of the soil moisture is 0.041.The retrieved soil moisture values in the case of test image

as well as the validation image show quite good agreement withthe observed ground truth value of soil moisture. The relation-ship between the observed and retrieved values of soil mois-ture for testing and validation phase has been shown in Figs. 8and 12 respectively. The minor difference in the RMSE of thetesting phase (i.e., 0.036) and the validation phase (i.e., 0.041)can be explained due to the varying nature of different vegeta-tion. However, the land cover of the both region are approxi-mately similar and the major vegetation cover during the monthof study (first week of April 2009) is sugarcane and wheat,


Fig. 9. PALSAR validating image (PAL–2, pixel spacing = 25 m). (a) Color composite image (HH = red, HV = green, VV = blue). (b) Classified image (red =urban, blue = water, green = short vegetation, sea green = long vegetation, and sienna bare soil). (c) Classified masked image with some of the area marked withcircle that was used for in situ measurement of moisture and roughness to validate the results.

Fig. 10. NDVI image of validating region retrieved through MOD-2 imagepixel spacing m .

the availability of different vegetable at small scale causes theminor differences in the RMSE of retrieved soil moisture.

VI. CONCLUSION

The study carried out in this paper acknowledges the problemof soil moisture retrieval in vegetated region and an algorithmbased on the information fusion approach of PALSAR, a SARdata and MODIS, an optical data is proposed to retrieve thesoil moisture over vegetated area. The PALSAR data was effi-ciently utilized with polarimetric capability to classify the land

Fig. 11. Soil moisture (volumetric) map of the validating image (PAL-2) wherepixel with black color show themasked area. The pixel spacing for the generatedsoil moisture map is 250 m.

cover in urban, water, vegetation and bare soil and subsequentlyto mask the urban and water region. The problem of vegeta-tion characterization in retrieval of soil moisture from SAR im-ages has been dealt with optical image by appropriately uti-lizing the NDVI, a vegetation indices, which describes the abun-dance of vegetation. The scattering coefficient of the PALSARdata was normalized and an empirical relationship was devel-oped with NDVI in order to provide the scattering coefficientof bare soil in HH- and VV-polarization. The minimization ofroughness effect in retrieval of soil moisture was performed by


Fig. 12. Observed and retrieved value of volumetric soil moisture for the val-idating area.

solving the Dubois equation with utilization of copolarizationratio approach. The developed approach was tested on the firstset of images in which the algorithm was developed and there-after validated on the second set of images. The obtained re-sults are quite encouraging and the methodology can be fur-ther used for retrieval of soil moisture with need of minimuma priori information. The proposed methodology may be quiteuseful for different radar sensors like RADARDAT, ENVISAT,RISAT (Radar imaging satellite to be launched by India in 2011)with use of MODIS data where minimum a priori informationis required.

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Rishi Prakash received the M.Sc. degree in elec-tronics from Lucknow University, India. He has sub-mitted his Ph.D. thesis in the Department of Elec-tronics and Computer Engineering, Indian Instituteof Technology Roorkee.His research interests are soil moisture study with

SAR data, SAR and optical data fusion to retrieve soilparameters, and study of soil parameters in bistaticdomain.

Dharmendra Singh (SM’10) was born in in Jaunpur,Uttar Pradesh, India. He received the Ph.D. degree inelectronics engineering from Banaras Hindu Univer-sity, Varanasi, India.He has more than 17 years of experience in

teaching and research. He was a Visiting ScientistPostdoctoral Fellow with the Information Engi-neering Department, Niigata University, Niigata,Japan; the German Aerospace Center, Germany; theInstitute for National Research in Informatics andAutomobile, France; Institute of Remote Sensing

Applications, Beijing, China; Karlsruhe University, Germany; and UPC,Barcelona, Spain. He also visited several other laboratories in other countries.He is currently an Associate Professor with the Department of Electronicsand Computer Engineering, Indian Institute of Technology Roorkee, Roorkee.He has published more than 150 papers in various national/international jour-nals and conferences His main research interests include microwave remotesensing, electromagnetic wave interaction with various media, polarimetricand interferometric applications of microwave data, and numerical modeling,ground penetrating radar and through wall imaging.Dr. Singh has received various fellowships and awards from national and

international bodies.

Nagendra P. Pathak born in Azamgarh, UttarPradesh, India. He received the B.Tech. and M.Tech.degrees in electronics and communication engi-neering from the University of Allahabad in 1996and 1998, respectively.He was Post Doctoral Research fellow at the

NRD Broadband Research Centre, Tohoku Instituteof Technology, Sendai, Japan. He joined the De-partment of Electronics and Computer Engineering,IIT Roorkee, as an Assistant Professor in 2006.His current research interests are development of

adoptable microwave/millimeter circuits and dielectric integrated guides atmillimeter-wave and optical frequencies.

a fusion approach to retrieve soil moisture with sar and optical

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